• Advances in environmental research
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• v.2 no.3
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• pp.203-227
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• 2013

Global demand for dwelling energy and implications of changing climatic conditions on buildings confront the built environment to build sustainable dwellings. This study investigates the variability of future climatic conditions on newly built detached dwellings in the UK. Series of energy modelling and simulations are performed on ten detached houses to evaluate and predict the impact of varying future climatic patterns on five building performance indicators. The study identifies and quantifies a consistent declining trend of building performance which is in consonance with current scientific knowledge of annual temperature change prediction in relations to long term climatic variation. The average percentage decrease for the annual energy consumption was predicted to be 2.80, 6.60 and 10.56 for 2020s, 2050s and 2080s time lines respectively. A similar declining trend in the case of annual natural gas consumption was 4.24, 9.98 and 16.1, and that for building emission rate and heating demand were 2.27, 5.49 and 8.72 and 7.82, 18.43 and 29.46 respectively. The study further analyse future heating and cooling demands of the three warmest months of the year and ascertain future variance in relative humidity and indoor temperature which might necessitate the use of room cooling systems to provide thermal comfort.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.457-465
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• 2015

The long lasting world-wide recession and low economic progress have made it more important to predict future economic behavior. Accordingly, it is of interest to explore useful leading indicators, correlated with policy targets, to predict future economic growth. This study attempts to develop a model to evaluate the performance of consumer survey results from Statistics Korea to predict future economic activities. A statistical model is formulated and estimated to generate predictions by utilizing consumer expectations. The prediction is found improved in the distant future and consumer expectations appear to be a useful leading indicator to provide information of future real growth.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.71-85
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• 2014

This study proposes the future strategy of semiconductor companies corresponding to the growth of cloud computing. Cloud computing is the delivery of IT resources such as hardware and software as a service rather than a product, and it is expected to significantly change the IT market. By employing the scenario planning method, this study develops a total of eight scenario cases, and presents the three possible scenarios including the best market, the worst market, and the neutral market scenario. This study suggests the future strategy of semiconductor companies based on the best market scenario (increasing firms' IT expenditure, increasing the complexity and performance of devices, the frequent replacement of devices). The suggested future strategy of semiconductor includes that the semiconductor companies need to strengthen their price competitiveness, secure the next generation technologies, and develop the better capability for market prediction with the growth of cloud computing. This study will help semiconductor companies set up the strategy direction of technology development, and understand the connections between cloud computing and the memory semiconductor industry. This study has practical implications for semiconductor industry to prepare for the future of cloud computing.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.755-764
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• 2011

In many companies field failure data is used to predict the future number of failures, especially when an unexpected failure mode happens to be a problem. It is because they want to predict the number of spare parts needed and the future quality warranty cost associated with the part based on the predictions of the future number of failures. In this paper field summary data is used to predict the future number of failures based on an appropriate distribution. Other types of data are also investigated to identify the appropriate distribution.

• Psychiatry investigation
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• v.15 no.11
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• pp.1037-1045
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• 2018

Objective The present study aimed to identify risk factors for future SI and to predict individual-level risk for future or persistent SI among college students. Methods Mental health check-up data collected over 3 years were retrospectively analyzed. Students were categorized as suicidal ideators and non-ideators at baseline. Logistic regression analyses were performed separately for each group, and the predicted probability for each student was calculated. Results Students likely to exhibit future SI had higher levels of mental health problems, including depression and anxiety, and significant risk factors for future SI included depression, current SI, social phobia, alcohol problems, being female, low self-esteem, and number of close relationships and concerns. Logistic regression models that included current suicide ideators revealed acceptable area under the curve (AUC) values (0.7-0.8) in both the receiver operating characteristic (ROC) and precision recall (PR) curves for predicting future SI. Predictive models with current suicide non-ideators revealed an acceptable level of AUCs only for ROC curves. Conclusion Several factors such as low self-esteem and a focus on short-term rather than long-term outcomes may enhance the prediction of future SI. Because a certain range of SI clearly necessitates clinical attention, further studies differentiating significant from other types of SI are necessary.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1115-1124
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• 2022

The impact of climate change on water resources was evaluated for Chungju Dam and Soyang-gang Dam basins by constructing an integrated modeling framework consisting of a dam inflow prediction model based on the Variable Infiltration Capacity (VIC) model, a distributed hydrologic model, and an LSTM based dam outflow prediction model. Considering the uncertainty of future climate data, four models of CMIP6 GCM were used as input data of VIC model for future period (2021-2100). As a result of applying future climate data, the average inflow for period increased as the future progressed, and the inflow in the far future (2070-2100) increased by up to 22% compared to that of the observation period (1986-2020). The minimum value of dam discharge lasting 4~50 days was significantly lower than the observed value. This indicates that droughts may occur over a longer period than observed in the past, meaning that citizens of Seoul metropolitan areas may experience severe water shortages due to future droughts. In addition, compared to the near and middle futures, the change in water storage has occurred rapidly in the far future, suggesting that the difficulties of water resource management may increase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Journal of Korean Academy of Nursing
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• v.42 no.6
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• pp.810-818
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• 2012

Purpose: This study was done to explore factors relating to number of falls among community-dwelling elders, based on gender. Methods: Participants were 403 older community dwellers (male=206, female=197) aged 60 or above. In this study, 8 variables were identified as predictive factors that can result in an elderly person falling and as such, supports previous studies. The 8 variables were categorized as, exogenous variables; perceived health status, somatization, depression, physical performance, and cognitive state, and endogenous variables; fear of falling, ADL & IADL and frequency of falls. Results: For men, ability to perform ADL & IADL (${\beta}_{32}$=1.84, p<.001) accounted for 16% of the variance in the number of falls. For women, fear of falling (${\beta}_{31}$=0.14, p<.05) and ability to perform ADL & IADL (${\beta}_{32}$=1.01, p<.001) significantly contributed to the number of falls, accounting for 15% of the variance in the number of falls. Conclusion: The findings from this study confirm the gender-based fall prediction model as comprehensive in relation to community-dwelling elders. The fall prediction model can effectively contribute to future studies in developing fall prediction and intervention programs.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.113-121
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• 2000

This paper presents a parallel-structure fuzzy system(PSFS) for prediction of time series data. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. The component fuzzy systems are characterized by multiple-input singleoutput( MIS0) Sugeno-type fuzzy rules modeled by clustering input-output product space data. The optimal embedding dimension for each component fuzzy system is chosen to have superior prediction performance for a given value of time delay. The PSFS determines the final prediction result by averaging the outputs of all the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.

• Water for future
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• v.18 no.2
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• pp.153-161
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• 1985

The methods for hydrologic prediction of streamflows for more efficient and functional operations and automation of the flood warning and forecasting system have been studiedand which have been widely used in the control engineering have been studied and investigated for representation of the dynamic behavior of rainfall-runoff precesses, and formulated into mathematical model form. The applicabilities of the model using the adaptive Kalman filter algorithm to the on-line, real-time prediction of river flows have been worked out. The computer programs in FORTRAN which are developed here can be utilized for more efficient operations and better prediction abilities of flood warning and forecasting systems, and also should be modified for better model performance.